| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Seattle Thunderbirds | WHL | 16 | 2 | 0 | 2 | 0.125 | 0.0608 | 0.0684 | 0.3067 | 0.3453 |
| 2022-23 | — | WHL | 53 | 4 | 3 | 7 | 0.132 | 0.0643 | 0.0694 | 0.3241 | 0.3497 |
| 2023-24 | Kelowna Rockets | WHL | 67 | 3 | 28 | 31 | 0.463 | 0.2251 | 0.2318 | 1.1353 | 1.1692 |
| 2024-25 | Vancouver Giants | WHL | 66 | 4 | 18 | 22 | 0.333 | 0.1622 | 0.1584 | 0.8178 | 0.7985 |
| 2025-26 | Vancouver Giants | WHL | 61 | 4 | 10 | 14 | 0.230 | 0.1117 | 0.1039 | 0.5631 | 0.5238 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.